Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Simultaneously Learning Stochastic and Adversarial Episodic MDPs with Known Transition
Authors: Tiancheng Jin, Haipeng Luo
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This work is mostly theoretical, with no negative outcomes. |
| Researcher Affiliation | Academia | Tiancheng Jin University of Southern California EMAIL Haipeng Luo University of Southern California EMAIL |
| Pseudocode | Yes | Our final algorithm is shown in Algorithm 1. |
| Open Source Code | No | The paper is theoretical and does not mention releasing any code or provide links to a repository. |
| Open Datasets | No | The paper is theoretical and does not involve empirical evaluation on datasets. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical evaluation or dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe any experiments that would require specific hardware specifications. |
| Software Dependencies | No | The paper is theoretical and does not describe any experiments that would require specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe any empirical experiment setup details such as hyperparameters or training configurations. |